DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.

Cytochrome P450 (P450)-mediated drug-drug interactions (DDIs) are responsible for most adverse drug interactions, and occur when 2 concurrently administered drugs inhibit, upregulate, or are substrates of the same target enzyme. A machine learning approach enables the detection of DDIs with rarely used drugs, as well as newly approved drugs. To facilitate this, we present a framework for predicting DDIs by first predicting P450 interactions for both drugs, generating a fingerprint based on the predictions in addition to the molecular structures of the drugs, and training a machine learning model to predict the overall interaction. After optimization, the model detected potential DDIs with 85% accuracy, representing an improvement over a DDI-only model (ie, a model trained on structure-based fingerprints without supporting P450 model predictions). We also present a corresponding adverse outcome pathway to allow for increased model explainability through visualizing each predicted P450 interaction, further enhancing its real-world applicability. Finally, we show the importance of the model applicability domain to DDI models by demonstrating how the performance of our model degrades as the inference set becomes dissimilar to the training data. SIGNIFICANCE STATEMENT: Polypharmacotherapy (especially in older populations) results in more drugs prescribed concurrently, creating an increased risk of drug-drug interactions and adverse drug reactions. Computational tools to predict potential drug-drug interactions could accurately aid in reducing risk for the patient and be used in the early stages of drug design to avoid such undesirable molecular interactions.